In this episode of the Digital Skill Podcast, Brad, Adam, and Max discuss Cursor, a development application similar to VS Code that has been a game changer in code shipping. They also talk about the differences in AI responses using LMSys.org, the story protocol on the blockchain, and the state of AI in the enterprise. The conversation segues into their recent vacations and their experiences at Disney and Universal.
They then dive into the features and benefits of Cursor, including code generation, multi-line edits, and the ability to reference code and documentation. They also explore the LM Sys leaderboard and compare different AI models for coding tasks. In this part of the conversation, Brad discusses the idea of using generative AI models to create and review code. He suggests a game where different models are tasked with creating and reviewing code, and the goal is to see how many iterations it takes to reach the best code.
They also discuss the controversy surrounding uncensored and copyright issues in generative AI, particularly in image creation. The conversation then shifts to the topic of intellectual property (IP) and copyrights in the age of AI, and the challenges of attributing and protecting original works.
They explore the potential of blockchain and other technologies to address these issues. Finally, they touch on a report by Deloitte that highlights the adoption and benefits of generative AI in enterprise organizations. The conversation explores the potential of generative AI and its impact on various industries. It discusses the ability of AI to predict user actions and improve efficiency in tasks. The conversation also highlights the shift of workers from lower to higher value tasks and the empowerment it brings. It emphasizes that AI is not meant to replace jobs but to enhance skills and enable individuals to do things they couldn't before.
The conversation touches on the importance of data strategy and management in the success of generative AI initiatives. It concludes with the need for clear metrics and evaluation to measure the value and productivity of AI implementations.
